Method, device, equipment and medium for scheduling optical storage system based on trigger mechanism

Abstract

The present invention relates to the technical field of photovoltaic storage system scheduling, and provides a photovoltaic storage system scheduling method, device, equipment and medium based on a trigger mechanism. The method, device, equipment and medium can construct multi-dimensional parameters with photovoltaic power generation prediction, load prediction, weather prediction, electricity price and energy storage of the photovoltaic storage system as multiple dimensions, and construct a day-ahead scheduling model based on the multi-dimensional parameters, so that a more reasonable day-ahead scheduling plan can be generated based on the multi-dimensional data; trigger conditions are detected in real time according to the execution data of the day-ahead scheduling plan to determine the trigger event, and scheduling optimization of the photovoltaic storage system is performed according to the trigger event, so that the trigger event can be dynamically detected, and targeted scheduling strategy adjustment and optimization are performed based on the multi-level trigger mechanism, which effectively improves the plan deviation problem caused by the uncertainty of photovoltaic and load in the scheduling process, improves the adaptability, anti-interference ability and operation efficiency of the photovoltaic storage system, and reduces the computational burden and improves the real-time performance.

Description

Technical Field

[0001] The present invention relates to a trigger mechanism-based scheduling of a photovoltaic storage system, and in particular to a trigger mechanism-based scheduling method, device, equipment and medium for a photovoltaic storage system. Background Art

[0002] With the rapid development of renewable energy, photovoltaic (PV) power generation and energy storage systems (ESS) are increasingly being used in power systems. Distributed PV-ESS systems, by combining PV power generation with energy storage, can effectively improve energy efficiency and enhance grid stability. However, due to the intermittent and fluctuating nature of PV power generation, optimizing the scheduling of energy storage systems is a key issue in ensuring efficient system operation.

[0003] Traditional scheduling methods for solar-to-storage systems typically create a schedule for the next day the previous day to ensure overall optimization. This schedule is then updated at fixed intervals in conjunction with real-time scheduling. However, this approach has certain limitations:

[0004] 1. Slow response to emergencies: The existing system has difficulty responding in real time to external signals such as electricity price fluctuations, demand response, and photovoltaic sudden changes;

[0005] 2. Increased server computing burden: Scheduled tasks are generally triggered at regular intervals. When triggered, this can cause a sudden increase in server computing load, which can easily cause server downtime.

[0006] 3. Inflexible scheduling: Most methods perform scheduling updates based on fixed time intervals (e.g., 15 minutes, 30 minutes), and are unable to dynamically respond to emergencies, resulting in reduced user benefits.

[0007] Therefore, there is an urgent need for a real-time scheduling method for photovoltaic storage systems with stronger adaptability, higher execution efficiency, and lower computational burden. Summary of the Invention

[0008] In view of the above, it is necessary to provide a method, device, equipment and medium for scheduling a photovoltaic storage system based on a trigger mechanism, aiming to solve the problems of low adaptability, low execution efficiency, high computational burden and inability to respond in real time in photovoltaic storage system scheduling.

[0009] A method for scheduling a photovoltaic storage system based on a trigger mechanism, the method comprising:

[0010] In response to a dispatch instruction for a target photovoltaic storage system, constructing multi-dimensional parameters based on photovoltaic power generation forecast, load forecast, weather forecast, electricity price and energy storage of the target photovoltaic storage system as multiple dimensions;

[0011] Constructing a day-ahead scheduling model of the target solar-storage system according to the multi-dimensional parameters;

[0012] generating a target day-ahead scheduling plan according to the day-ahead scheduling model, and sending the target day-ahead scheduling plan to the target solar-to-storage system for execution;

[0013] Detecting trigger conditions in real time based on the execution data of the target day-ahead scheduling plan, and determining a trigger event based on the trigger conditions;

[0014] Scheduling optimization of the target solar-to-storage system is performed according to the triggering event.

[0015] According to a preferred embodiment of the present invention, constructing the day-ahead scheduling model of the target solar-storage system according to the multi-dimensional parameters includes:

[0016] Constructing an objective function of the day-ahead scheduling model;

[0017] Constructing constraints for the day-ahead scheduling model;

[0018] Constructing decision variables of the day-ahead scheduling model;

[0019] The objective function, the constraints and the decision variables are integrated to obtain the day-ahead scheduling model.

[0020] According to a preferred embodiment of the present invention, the objective function of constructing the day-ahead scheduling model includes:

[0021] The following formula is used to construct the day-ahead scheduling model:

[0022] ;

[0023] in, represents the maximum benefit of the target solar-storage system; represents the electricity price sold by the target PV-storage system to the power market at time t; represents the power sold by the target PV-storage system to the power market at time t; represents the electricity purchase price of the target PV-storage system from the power market at time t; represents the power purchased by the target PV-storage system from the power market at time t; T represents the maximum value of t;

[0024] Wherein, at the same time, the target solar-to-storage system is in a power purchasing state or a power selling state.

[0025] According to a preferred embodiment of the present invention, the constraints for constructing the day-ahead scheduling model include:

[0026] The following formula is used to construct the constraints of the day-ahead scheduling model:

[0027] ;

[0028] in, represents a first state quantity, wherein the first state quantity is used to characterize the interaction state between the target photovoltaic storage system and the power grid; represents the predicted photovoltaic power generation power of the target photovoltaic storage system at time t; represents the load prediction power of the target solar-storage system at time t; Indicates the charge and discharge power of the energy storage battery of the target solar-storage system; Indicates the charging power of the energy storage battery of the target solar-storage system; Indicates the discharge power of the energy storage battery of the target solar-storage system; represents a second state quantity, wherein the second state quantity is used to characterize the charge and discharge state of the energy storage battery of the target photovoltaic storage system; Indicates the maximum discharge power of the energy storage battery of the target solar-storage system; Indicates the maximum charging power of the energy storage battery of the target solar-storage system; Indicates the state of charge of the energy storage battery of the target solar-storage system at time (t+1); Indicates the initial state of charge of the energy storage battery of the target solar-storage system; Indicates the charging efficiency of the energy storage battery of the target solar-storage system; Indicates the discharge efficiency of the energy storage battery of the target solar-storage system; Indicates the capacity of the energy storage battery of the target solar-storage system; Indicates a time interval; Indicates the minimum state of charge of the energy storage battery of the target solar-storage system; Indicates the maximum state of charge of the energy storage battery of the target solar-storage system; represents the rated photovoltaic power generation power of the target photovoltaic storage system;

[0029] Wherein, at the same time, the energy storage battery of the target photovoltaic storage system is in a charging state or a discharging state.

[0030] According to a preferred embodiment of the present invention, generating a target day-ahead scheduling plan according to the day-ahead scheduling model includes:

[0031] Get each variable in the decision variables; wherein the decision variables are ;

[0032] Performing data collection according to the multi-dimensional parameters;

[0033] Input the collected data into the day-ahead scheduling model to obtain the value of each variable;

[0034] The target day-ahead scheduling plan is generated according to the value of each variable.

[0035] According to a preferred embodiment of the present invention, detecting a trigger condition in real time based on the execution data of the target day-ahead scheduling plan, and determining a trigger event based on the trigger condition includes:

[0036] Calculate the power deviation between the actual photovoltaic power generation power and the predicted photovoltaic power generation power of the target photovoltaic storage system at time t according to the execution data ;in, represents the actual photovoltaic power generation of the target photovoltaic storage system at time t;

[0037] Calculate the state of charge deviation of the energy storage battery of the target photovoltaic storage system at time t according to the execution data ;in, represents the actual state of charge of the energy storage battery of the target solar-storage system at time t, represents the state of charge of the energy storage battery of the target solar-storage system in the target day-ahead scheduling plan at time t;

[0038] Calculate the load power deviation of the target solar-storage system at time t based on the execution data ;in, represents the actual load power of the target solar-storage system at time t;

[0039] When it is detected that the triggering condition is sudden extreme weather, and / or emergency power outage, and / or the need to activate a backup power supply, determining that the triggering event is a level one response event; or

[0040] When it is detected that the power deviation is greater than a first threshold, and / or the state of charge deviation is greater than a second threshold, and / or the load power deviation is greater than a third threshold, the triggering event is determined to be a secondary response event.

[0041] According to a preferred embodiment of the present invention, performing scheduling optimization on the target solar-storage system according to the triggering event includes:

[0042] When the triggering event is the first-level response event, starting the backup power supply to supply power based on the fast compensation algorithm; or

[0043] When the triggering event is the secondary response event, multiple time steps of the day-ahead scheduling model are obtained; a preset number of consecutive time steps starting from the current moment are obtained from the multiple time steps as each time step to be processed; a new day-ahead scheduling plan is generated at each time step to be processed according to the scheduling model; for each time step to be processed, when the new day-ahead scheduling plan is the same as the target day-ahead scheduling plan, the target day-ahead scheduling plan for the corresponding time step to be processed is not adjusted; or when the new day-ahead scheduling plan is different from the target day-ahead scheduling plan, the target day-ahead scheduling plan for the corresponding time step to be processed is replaced with the new day-ahead scheduling plan.

[0044] A photovoltaic storage system scheduling device based on a trigger mechanism, the photovoltaic storage system scheduling device based on a trigger mechanism comprising:

[0045] a constructing unit, configured to construct multi-dimensional parameters based on photovoltaic power generation forecast, load forecast, weather forecast, electricity price and energy storage of the target photovoltaic power storage system in response to a dispatch instruction to the target photovoltaic power storage system;

[0046] The construction unit is further configured to construct a day-ahead scheduling model for the target solar-storage system based on the multi-dimensional parameters;

[0047] a generating unit, configured to generate a target day-ahead scheduling plan according to the day-ahead scheduling model, and send the target day-ahead scheduling plan to the target solar-to-storage system for execution of the target day-ahead scheduling plan;

[0048] a detection unit, configured to detect trigger conditions in real time based on the execution data of the target day-ahead scheduling plan, and determine a trigger event based on the trigger conditions;

[0049] An execution unit is configured to execute scheduling optimization of the target solar-storage system according to the triggering event.

[0050] A computer device, comprising:

[0051] a memory storing at least one instruction; and

[0052] The processor executes the instructions stored in the memory to implement the trigger mechanism-based photovoltaic storage system scheduling method.

[0053] A computer-readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor in a computer device to implement the trigger mechanism-based optical storage system scheduling method.

[0054] It can be seen from the above technical solutions that the present invention can construct multi-dimensional parameters with the photovoltaic power generation forecast, load forecast, weather forecast, electricity price and energy storage of the target photovoltaic storage system as multiple dimensions, and construct a day-ahead scheduling model based on the multi-dimensional parameters, so that a more reasonable target day-ahead scheduling plan can be generated based on the multi-dimensional data; the trigger conditions are detected in real time according to the execution data of the target day-ahead scheduling plan, the trigger events are determined according to the trigger conditions, and the scheduling optimization of the photovoltaic storage system is performed according to the trigger events, so that the trigger events can be dynamically detected, and the scheduling strategy can be adjusted and optimized in a targeted manner based on the multi-level trigger mechanism, which effectively improves the plan deviation problem caused by the uncertainty of photovoltaic and load in the scheduling process, improves the adaptability, anti-interference ability and operation efficiency of the photovoltaic storage system, and at the same time reduces the computational burden and improves real-time performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of a preferred embodiment of the solar-storage system scheduling method based on the trigger mechanism of the present invention;

[0056] Figure 2 This is a functional module diagram of a preferred embodiment of the solar-storage system scheduling device based on a trigger mechanism of the present invention;

[0057] Figure 3 It is a structural diagram of a computer device of a preferred embodiment of the present invention for implementing a method for scheduling a photovoltaic storage system based on a trigger mechanism. DETAILED DESCRIPTION

[0058] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0059] like Figure 1 FIG. 1 is a flow chart of a preferred embodiment of a method for scheduling a solar-storage system based on a trigger mechanism according to the present invention. The order of the steps in the flow chart can be changed and some steps can be omitted according to different requirements.

[0060] The trigger-based scheduling method for an optical storage system is applied to one or more computer devices. The computer device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes but is not limited to a microprocessor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a digital signal processor (DSP), an embedded device, etc.

[0061] The computer device may be any electronic product that can perform human-computer interaction with a user, such as a personal computer, a tablet computer, a smart phone, a personal digital assistant (PDA), a game console, an interactive network television (IPTV), a smart wearable device, etc.

[0062] The computer device may also include a network device and / or a user device, wherein the network device includes, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of hosts or network servers.

[0063] The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0064] Among them, Artificial Intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0065] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0066] The network where the computer device is located includes but is not limited to the Internet, wide area network, metropolitan area network, local area network, virtual private network (VPN), etc.

[0067] S10 , in response to a dispatch instruction to a target photovoltaic storage system, constructing multi-dimensional parameters with photovoltaic power generation forecast, load forecast, weather forecast, electricity price and energy storage of the target photovoltaic storage system as multiple dimensions.

[0068] In this embodiment, the target solar-energy storage system is a power generation system consisting of a photovoltaic device and an energy storage device. The target solar-energy storage system directly converts solar radiation energy into electrical energy using the photovoltaic effect and stores excess electrical energy using the energy storage device.

[0069] In this embodiment, the scheduling instruction may be automatically triggered when the target solar-energy storage system is started, so as to achieve comprehensive monitoring of the target solar-energy storage system.

[0070] S11, constructing a day-ahead scheduling model of the target solar-to-storage system according to the multi-dimensional parameters.

[0071] In this embodiment, constructing the day-ahead scheduling model of the target solar-to-storage system according to the multi-dimensional parameters includes:

[0072] Constructing an objective function of the day-ahead scheduling model;

[0073] Constructing constraints for the day-ahead scheduling model;

[0074] Constructing decision variables of the day-ahead scheduling model;

[0075] The objective function, the constraints and the decision variables are integrated to obtain the day-ahead scheduling model.

[0076] The objective function of constructing the day-ahead scheduling model includes:

[0077] The following formula is used to construct the day-ahead scheduling model: ;

[0078] in, represents the maximum benefit of the target solar-storage system; represents the electricity price sold by the target PV-storage system to the power market at time t; represents the power sold by the target PV-storage system to the power market at time t; represents the electricity purchase price of the target PV-storage system from the power market at time t; represents the power purchased by the target PV-storage system from the power market at time t; T represents the maximum value of t;

[0079] Wherein, at the same time, the target solar-to-storage system is in a power purchasing state or a power selling state.

[0080] The constraints for constructing the day-ahead scheduling model include:

[0081] The following formula is used to construct the constraints of the day-ahead scheduling model:

[0082] ;

[0083] in, represents a first state quantity, wherein the first state quantity is used to characterize the interaction state between the target photovoltaic storage system and the power grid; represents the predicted photovoltaic power generation power of the target photovoltaic storage system at time t; represents the load prediction power of the target solar-storage system at time t; Indicates the charge and discharge power of the energy storage battery of the target solar-storage system; Indicates the charging power of the energy storage battery of the target solar-storage system; Indicates the discharge power of the energy storage battery of the target solar-storage system; represents a second state quantity, wherein the second state quantity is used to characterize the charge and discharge state of the energy storage battery of the target photovoltaic storage system; Indicates the maximum discharge power of the energy storage battery of the target solar-storage system; Indicates the maximum charging power of the energy storage battery of the target solar-storage system; Indicates the state of charge of the energy storage battery of the target solar-storage system at time (t+1); Indicates the initial state of charge of the energy storage battery of the target solar-storage system; Indicates the charging efficiency of the energy storage battery of the target solar-storage system; Indicates the discharge efficiency of the energy storage battery of the target solar-storage system; Indicates the capacity of the energy storage battery of the target solar-storage system; Indicates a time interval; Indicates the minimum state of charge of the energy storage battery of the target solar-storage system; Indicates the maximum state of charge of the energy storage battery of the target solar-storage system; represents the rated photovoltaic power generation power of the target photovoltaic storage system;

[0084] Wherein, at the same time, the energy storage battery of the target photovoltaic storage system is in a charging state or a discharging state.

[0085] Specifically, by deploying a multi-source data fusion engine, event characteristics such as photovoltaic output gradient changes (dP / dt), energy storage SOC (State of Charge) exceeding the limit, SOC deviation, load mutation, etc. can be captured in real time as the multi-dimensional parameters.

[0086] Through the above embodiments, a day-ahead scheduling model can be constructed based on multi-dimensional parameters, thereby assisting in more reasonably generating a day-ahead scheduling plan.

[0087] S12 , generating a target day-ahead scheduling plan according to the day-ahead scheduling model, and sending the target day-ahead scheduling plan to the target solar-plus-storage system for execution.

[0088] In this embodiment, generating a target day-ahead scheduling plan according to the day-ahead scheduling model includes:

[0089] Get each variable in the decision variables; wherein the decision variables are ;

[0090] Performing data collection according to the multi-dimensional parameters;

[0091] Input the collected data into the day-ahead scheduling model to obtain the value of each variable;

[0092] The target day-ahead scheduling plan is generated according to the value of each variable.

[0093] Among them, a dynamic programming solver can be used for solving, such as Cplex, Gurobi, etc.

[0094] The state of charge of the energy storage battery of the target photovoltaic storage system at time t in the target day-ahead scheduling plan can also be calculated based on the solved decision variables.

[0095] In the above embodiment, since there are errors in both photovoltaic and load forecasts, and some emergencies may occur during the actual implementation process, this embodiment introduces an intraday or real-time scheduling model based on event and time triggering mechanism to improve the rationality and accuracy of the generated day-ahead scheduling plan.

[0096] S13: detecting trigger conditions in real time according to the execution data of the target day-ahead scheduling plan, and determining a trigger event according to the trigger conditions.

[0097] In this embodiment, after the target day-ahead scheduling plan is sent to the target solar-plus-storage system to execute the target day-ahead scheduling plan, the real-time condition judgment mechanism can be triggered.

[0098] In this embodiment, detecting a trigger condition in real time based on the execution data of the target day scheduling plan and determining a trigger event based on the trigger condition includes:

[0099] Calculate the power deviation between the actual photovoltaic power generation power and the predicted photovoltaic power generation power of the target photovoltaic storage system at time t according to the execution data ;in, represents the actual photovoltaic power generation of the target photovoltaic storage system at time t;

[0100] Calculate the state of charge deviation of the energy storage battery of the target photovoltaic storage system at time t according to the execution data ;in, represents the actual state of charge of the energy storage battery of the target solar-storage system at time t, represents the state of charge of the energy storage battery of the target solar-storage system in the target day-ahead scheduling plan at time t;

[0101] Calculate the load power deviation of the target solar-storage system at time t based on the execution data ;in, represents the actual load power of the target solar-storage system at time t;

[0102] When it is detected that the triggering condition is sudden extreme weather, and / or emergency power outage, and / or the need to activate a backup power supply, determining that the triggering event is a level one response event; or

[0103] When it is detected that the power deviation is greater than a first threshold, and / or the state of charge deviation is greater than a second threshold, and / or the load power deviation is greater than a third threshold, the triggering event is determined to be a secondary response event.

[0104] The first threshold, the second threshold, and the third threshold can be customized. For example, the first threshold can be configured as 15%, the second threshold can be configured as 15%, and the third threshold can be configured as 0.2.

[0105] This embodiment can combine time-driven and event-driven reasoning to perform coupled impact assessment on compound events (such as a sudden drop in photovoltaic power and a sudden increase in load).

[0106] S14: Execute scheduling optimization of the target solar-to-storage system according to the triggering event.

[0107] In this embodiment, performing scheduling optimization on the target solar-storage system according to the triggering event includes:

[0108] When the triggering event is the first-level response event, starting the backup power supply to supply power based on the fast compensation algorithm; or

[0109] When the triggering event is the secondary response event, multiple time steps of the day-ahead scheduling model are obtained; a preset number of consecutive time steps starting from the current moment are obtained from the multiple time steps as each time step to be processed; a new day-ahead scheduling plan is generated at each time step to be processed according to the scheduling model; for each time step to be processed, when the new day-ahead scheduling plan is the same as the target day-ahead scheduling plan, the target day-ahead scheduling plan for the corresponding time step to be processed is not adjusted; or when the new day-ahead scheduling plan is different from the target day-ahead scheduling plan, the target day-ahead scheduling plan for the corresponding time step to be processed is replaced with the new day-ahead scheduling plan.

[0110] The day-ahead scheduling model may include 24 time steps per day. If the current time step is the first, and the preset number is 5, data is collected again from the second to the sixth time steps to generate a new day-ahead scheduling plan. If the day-ahead scheduling plan regenerated in one of the time steps is the same as the original plan, no further operations are performed. If the day-ahead scheduling plan regenerated in one of the time steps is different from the original plan, the new day-ahead scheduling plan is used to overwrite the original plan, thereby optimizing the day-ahead scheduling plan based on the event triggering mechanism.

[0111] Among them, if the first-level response event or the second-level response event does not occur, the original day-ahead scheduling plan will be maintained.

[0112] Among them, a hierarchical optimization approach is adopted. The upper layer adopts a long-cycle economic scheduling model (such as a 24-hour scale), and the lower layer embeds an event-triggered real-time correction model (which can reach the second scale). A dual-mode optimization algorithm of "rolling time domain + event replanning" is proposed. Predictive scheduling is performed under normal operating conditions, and local reoptimization is initiated when an event is triggered. This can optimize the scheduling plan of the photovoltaic storage system more accurately, flexibly and reasonably.

[0113] Through the above embodiments, a multi-level trigger mechanism is used to detect changes in photovoltaic power generation, energy storage status, load demand, electricity price signals, etc., and a scheduling optimization strategy is dynamically executed, which greatly improves the plan deviation caused by photovoltaic and load uncertainties, improves the system's adaptability, anti-interference ability and operating efficiency, and at the same time can reduce the computational burden and improve real-time performance.

[0114] This embodiment can be applied to fields such as smart microgrids.

[0115] It can be seen from the above technical solutions that the present invention can construct multi-dimensional parameters with the photovoltaic power generation forecast, load forecast, weather forecast, electricity price and energy storage of the target photovoltaic storage system as multiple dimensions, and construct a day-ahead scheduling model based on the multi-dimensional parameters, so that a more reasonable target day-ahead scheduling plan can be generated based on the multi-dimensional data; the trigger conditions are detected in real time according to the execution data of the target day-ahead scheduling plan, the trigger events are determined according to the trigger conditions, and the scheduling optimization of the photovoltaic storage system is performed according to the trigger events, so that the trigger events can be dynamically detected, and the scheduling strategy can be adjusted and optimized in a targeted manner based on the multi-level trigger mechanism, which effectively improves the plan deviation problem caused by the uncertainty of photovoltaic and load in the scheduling process, improves the adaptability, anti-interference ability and operation efficiency of the photovoltaic storage system, and at the same time reduces the computational burden and improves real-time performance.

[0116] like Figure 2 The figure shows a functional block diagram of a preferred embodiment of a trigger-based solar-to-storage system scheduling device according to the present invention. The trigger-based solar-to-storage system scheduling device 11 comprises a construction unit 110, a generation unit 111, a detection unit 112, and an execution unit 113. As used herein, a module / unit refers to a series of computer program segments that can be executed by a processor and perform a fixed function, stored in memory. The functions of each module / unit in this embodiment will be described in detail in subsequent embodiments.

[0117] The construction unit is configured to construct multi-dimensional parameters based on the photovoltaic power generation forecast, load forecast, weather forecast, electricity price and energy storage of the target photovoltaic power storage system in response to the dispatch instruction of the target photovoltaic power storage system;

[0118] The construction unit is further configured to construct a day-ahead scheduling model for the target solar-storage system based on the multi-dimensional parameters;

[0119] a generating unit, configured to generate a target day-ahead scheduling plan according to the day-ahead scheduling model, and send the target day-ahead scheduling plan to the target solar-to-storage system for execution of the target day-ahead scheduling plan;

[0120] a detection unit, configured to detect trigger conditions in real time based on the execution data of the target day-ahead scheduling plan, and determine a trigger event based on the trigger conditions;

[0121] An execution unit is configured to execute scheduling optimization of the target solar-storage system according to the triggering event.

[0122] It can be seen from the above technical solutions that the present invention can construct multi-dimensional parameters with the photovoltaic power generation forecast, load forecast, weather forecast, electricity price and energy storage of the target photovoltaic storage system as multiple dimensions, and construct a day-ahead scheduling model based on the multi-dimensional parameters, so that a more reasonable target day-ahead scheduling plan can be generated based on the multi-dimensional data; the trigger conditions are detected in real time according to the execution data of the target day-ahead scheduling plan, the trigger events are determined according to the trigger conditions, and the scheduling optimization of the photovoltaic storage system is performed according to the trigger events, so that the trigger events can be dynamically detected, and the scheduling strategy can be adjusted and optimized in a targeted manner based on the multi-level trigger mechanism, which effectively improves the plan deviation problem caused by the uncertainty of photovoltaic and load in the scheduling process, improves the adaptability, anti-interference ability and operation efficiency of the photovoltaic storage system, and at the same time reduces the computational burden and improves real-time performance.

[0123] like Figure 3 FIG. 1 is a schematic diagram of the structure of a computer device according to a preferred embodiment of the present invention for implementing a method for scheduling a photovoltaic storage system based on a trigger mechanism.

[0124] The computer device 1 may include a memory 12, a processor 13 and a bus (the arrow in the figure represents the bus), and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a trigger-based scheduling program for an optical storage system.

[0125] Those skilled in the art will understand that the schematic diagram is merely an example of the computer device 1 and does not constitute a limitation on the computer device 1. The computer device 1 may have either a bus structure or a star structure. The computer device 1 may also include more or less other hardware or software than shown in the figure, or a different arrangement of components. For example, the computer device 1 may also include input and output devices, network access devices, etc.

[0126] It should be noted that the computer device 1 is only an example. Other existing or future electronic products that are suitable for the present invention should also be included in the scope of protection of the present invention and included here by reference.

[0127] The memory 12 includes at least one type of readable storage medium, including flash memory, a removable hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 12 may be an internal storage unit of the computer device 1, such as a removable hard disk of the computer device 1. In other embodiments, the memory 12 may also be an external storage device of the computer device 1, such as a plug-in removable hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the memory 12 may include both an internal storage unit of the computer device 1 and an external storage device. The memory 12 can be used not only to store application software installed in the computer device 1 and various types of data, such as the code of the trigger-based optical storage system scheduler, but also to temporarily store data that has been output or is about to be output.

[0128] In some embodiments, the processor 13 may be comprised of an integrated circuit, such as a single packaged integrated circuit or multiple packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 13 is the control core (control unit) of the computer device 1, connecting the various components of the computer device 1 using various interfaces and circuits. It executes programs or modules stored in the memory 12 (e.g., executing a trigger-based scheduling program for an optical storage system) and accesses data stored in the memory 12 to perform various functions and process data.

[0129] The processor 13 executes the operating system of the computer device 1 and various installed applications. The processor 13 executes the applications to implement the steps in the above embodiments of the trigger mechanism-based solar-storage system scheduling method, for example Figure 1 Steps shown.

[0130] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory 12 and executed by the processor 13 to implement the present invention. The one or more modules / units may be a series of computer-readable instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the computer device 1. For example, the computer program may be divided into a construction unit 110, a generation unit 111, a detection unit 112, and an execution unit 113.

[0131] The integrated unit implemented as a software functional module can be stored in a computer-readable storage medium. The software functional module stored in the storage medium includes instructions for causing a computer device (which can be a personal computer, computer equipment, or network equipment, etc.) or a processor to execute the trigger-based scheduling method for a photovoltaic storage system described in various embodiments of the present invention.

[0132] If the modules / units integrated in the computer device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present invention can also implement all or part of the processes in the above-mentioned method embodiments by instructing relevant hardware devices through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments.

[0133] The computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium may include any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory, etc.

[0134] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the blockchain node, etc.

[0135] Blockchain, as used in this article, refers to a novel application model for computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Blockchain is essentially a decentralized database, a series of data blocks linked together using cryptographic methods. Each block contains information about a batch of online transactions, used to verify the validity of this information (to prevent counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, the platform product and service layer, and the application service layer.

[0136] The bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The figure shows that only one straight line is used, but it does not mean that there is only one bus or one type of bus. The bus is configured to realize the connection and communication between the memory 12 and at least one processor 13.

[0137] Although not shown, the computer device 1 may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to the at least one processor 13 via a power management device, thereby enabling functions such as charge management, discharge management, and power consumption management through the power management device. The power supply may also include one or more DC or AC power supplies, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components. The computer device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be further described here.

[0138] Furthermore, the computer device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the computer device 1 and other computer devices.

[0139] Optionally, the computer device 1 may further include a user interface, which may be a display or an input unit (such as a keyboard). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display may also be appropriately referred to as a display screen or display unit, and is used to display information processed by the computer device 1 and to display a visual user interface.

[0140] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0141] It will be understood by those skilled in the art that Figure 3 The structure shown does not constitute a limitation on the computer device 1 , and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0142] Combine Figure 1 The memory 12 in the computer device 1 stores a plurality of instructions to implement a method for scheduling a photovoltaic storage system based on a trigger mechanism, and the processor 13 can execute the plurality of instructions to implement:

[0143] In response to a dispatch instruction for a target photovoltaic storage system, constructing multi-dimensional parameters based on photovoltaic power generation forecast, load forecast, weather forecast, electricity price and energy storage of the target photovoltaic storage system as multiple dimensions;

[0144] Constructing a day-ahead scheduling model of the target solar-storage system according to the multi-dimensional parameters;

[0145] generating a target day-ahead scheduling plan according to the day-ahead scheduling model, and sending the target day-ahead scheduling plan to the target solar-to-storage system for execution;

[0146] Detecting trigger conditions in real time based on the execution data of the target day-ahead scheduling plan, and determining a trigger event based on the trigger conditions;

[0147] Scheduling optimization of the target solar-to-storage system is performed according to the triggering event.

[0148] Specifically, the specific implementation method of the processor 13 for the above instructions can refer to Figure 1 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0149] It should be noted that the data involved in this case were all obtained legally. The software tools or components not produced by our company that appear in the embodiments of this application are merely examples and do not represent actual use.

[0150] In the several embodiments provided herein, it should be understood that the disclosed systems, devices, and methods may be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical functional division, and actual implementation may employ other division methods.

[0151] The present invention can be used in a wide variety of general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0152] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0153] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0154] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0155] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0156] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices described in the present invention may also be implemented by a single unit or device through software or hardware. Terms such as first and second are used to indicate names and do not imply any particular order.

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for scheduling a solar-storage system based on a trigger mechanism, characterized in that: The trigger mechanism-based scheduling method for a photovoltaic storage system includes: In response to a dispatch instruction for a target photovoltaic storage system, constructing multi-dimensional parameters based on photovoltaic power generation forecast, load forecast, weather forecast, electricity price and energy storage of the target photovoltaic storage system as multiple dimensions; Constructing a day-ahead scheduling model of the target solar-storage system according to the multi-dimensional parameters; generating a target day-ahead scheduling plan according to the day-ahead scheduling model, and sending the target day-ahead scheduling plan to the target solar-to-storage system for execution; Detecting trigger conditions in real time based on the execution data of the target day-ahead scheduling plan, and determining a trigger event based on the trigger conditions; Executing scheduling optimization of the target photovoltaic storage system according to the triggering event, including: when the triggering event is a first-level response event, starting a backup power supply to supply power based on a fast compensation algorithm; or when the triggering event is a second-level response event, obtaining multiple time steps of the day-ahead scheduling model; obtaining a preset number of consecutive time steps from the current moment as each time step to be processed from the multiple time steps; generating a new day-ahead scheduling plan at each time step to be processed according to the day-ahead scheduling model; for each time step to be processed, when the new day-ahead scheduling plan is the same as the target day-ahead scheduling plan, not adjusting the target day-ahead scheduling plan corresponding to the time step to be processed, or when the new day-ahead scheduling plan is different from the target day-ahead scheduling plan, replacing the target day-ahead scheduling plan corresponding to the time step to be processed with the new day-ahead scheduling plan; The detecting of trigger conditions in real time based on the execution data of the target day-ahead scheduling plan and determining a trigger event based on the trigger conditions includes: Calculate the power deviation between the actual photovoltaic power generation power and the predicted photovoltaic power generation power of the target photovoltaic storage system at time t according to the execution data : ;in, represents the actual photovoltaic power generation of the target photovoltaic storage system at time t; represents the predicted photovoltaic power generation power of the target photovoltaic storage system at time t; Calculate the state of charge deviation of the energy storage battery of the target photovoltaic storage system at time t according to the execution data : ;in, represents the actual state of charge of the energy storage battery of the target solar-storage system at time t, represents the state of charge of the energy storage battery of the target solar-storage system in the target day-ahead scheduling plan at time t; Calculate the load power deviation of the target solar-storage system at time t based on the execution data : ;in, represents the actual load power of the target solar-storage system at time t; represents the load prediction power of the target solar-storage system at time t; When it is detected that the triggering condition is sudden extreme weather, and / or emergency power outage, and / or the need to activate a backup power supply, determining that the triggering event is the first-level response event; or When it is detected that the power deviation is greater than a first threshold, and / or the state of charge deviation is greater than a second threshold, and / or the load power deviation is greater than a third threshold, the triggering event is determined to be the secondary response event.

2. The method for scheduling a photovoltaic storage system based on a trigger mechanism according to claim 1, wherein: The step of constructing the day-ahead scheduling model of the target solar-storage system according to the multi-dimensional parameters includes: Constructing an objective function of the day-ahead scheduling model; Constructing constraints for the day-ahead scheduling model; Constructing decision variables of the day-ahead scheduling model; The objective function, the constraints and the decision variables are integrated to obtain the day-ahead scheduling model.

3. The trigger mechanism-based scheduling method for a photovoltaic storage system according to claim 2, wherein: The objective function of constructing the day-ahead scheduling model includes: The following formula is used to construct the day-ahead scheduling model: ； in, represents the maximum benefit of the target solar-storage system; represents the electricity price sold by the target PV-storage system to the power market at time t; represents the power sold by the target PV-storage system to the power market at time t; represents the electricity purchase price of the target PV-storage system from the power market at time t; represents the power purchased by the target PV-storage system from the power market at time t; T represents the maximum value of t; Wherein, at the same time, the target solar-to-storage system is in a power purchasing state or a power selling state.

4. The method for scheduling a photovoltaic storage system based on a trigger mechanism according to claim 3, wherein: The constraints for constructing the day-ahead scheduling model include: The following formula is used to construct the constraints of the day-ahead scheduling model: ； in, represents a first state quantity, wherein the first state quantity is used to characterize the interaction state between the target photovoltaic storage system and the power grid; Indicates the charge and discharge power of the energy storage battery of the target solar-storage system; Indicates the charging power of the energy storage battery of the target solar-storage system; Indicates the discharge power of the energy storage battery of the target solar-storage system; represents a second state quantity, wherein the second state quantity is used to characterize the charge and discharge state of the energy storage battery of the target photovoltaic storage system; Indicates the maximum discharge power of the energy storage battery of the target solar-storage system; Indicates the maximum charging power of the energy storage battery of the target solar-storage system; Indicates the state of charge of the energy storage battery of the target solar-storage system at time (t+1); Indicates the initial state of charge of the energy storage battery of the target solar-storage system; Indicates the charging efficiency of the energy storage battery of the target solar-storage system; Indicates the discharge efficiency of the energy storage battery of the target solar-storage system; Represents the capacity of the energy storage battery of the target solar-storage system; Indicates a time interval; Indicates the minimum state of charge of the energy storage battery of the target solar-storage system; Indicates the maximum state of charge of the energy storage battery of the target solar-storage system; represents the rated photovoltaic power generation power of the target photovoltaic storage system; Wherein, at the same time, the energy storage battery of the target photovoltaic storage system is in a charging state or a discharging state.

5. The method for scheduling a photovoltaic storage system based on a trigger mechanism according to claim 4, wherein: Generating a target day-ahead scheduling plan according to the day-ahead scheduling model includes: Get each variable in the decision variables; wherein the decision variables are ; Performing data collection according to the multi-dimensional parameters; Input the collected data into the day-ahead scheduling model to obtain the value of each variable; The target day-ahead scheduling plan is generated according to the value of each variable.

6. A solar-storage system scheduling device based on a trigger mechanism, characterized in that: The trigger mechanism-based scheduling device for the photovoltaic storage system includes: a constructing unit, configured to construct multi-dimensional parameters based on photovoltaic power generation forecast, load forecast, weather forecast, electricity price and energy storage of the target photovoltaic power storage system in response to a dispatch instruction to the target photovoltaic power storage system; The construction unit is further configured to construct a day-ahead scheduling model for the target solar-storage system based on the multi-dimensional parameters; a generating unit, configured to generate a target day-ahead scheduling plan according to the day-ahead scheduling model, and send the target day-ahead scheduling plan to the target solar-to-storage system for execution of the target day-ahead scheduling plan; a detection unit, configured to detect trigger conditions in real time based on the execution data of the target day-ahead scheduling plan, and determine a trigger event based on the trigger conditions; an execution unit, configured to perform scheduling optimization of the target photovoltaic storage system according to the triggering event, including: when the triggering event is a first-level response event, starting a backup power supply to supply power based on a fast compensation algorithm; or when the triggering event is a second-level response event, obtaining multiple time steps of the day-ahead scheduling model; obtaining a preset number of consecutive time steps from the current moment as each time step to be processed from the multiple time steps; generating a new day-ahead scheduling plan at each time step to be processed according to the day-ahead scheduling model; for each time step to be processed, when the new day-ahead scheduling plan is the same as the target day-ahead scheduling plan, not adjusting the target day-ahead scheduling plan corresponding to the time step to be processed, or when the new day-ahead scheduling plan is different from the target day-ahead scheduling plan, replacing the target day-ahead scheduling plan corresponding to the time step to be processed with the new day-ahead scheduling plan; The detecting of trigger conditions in real time based on the execution data of the target day-ahead scheduling plan and determining a trigger event based on the trigger conditions includes: Calculate the power deviation between the actual photovoltaic power generation power and the predicted photovoltaic power generation power of the target photovoltaic storage system at time t according to the execution data : ;in, represents the actual photovoltaic power generation of the target photovoltaic storage system at time t; represents the predicted photovoltaic power generation power of the target photovoltaic storage system at time t; Calculate the state of charge deviation of the energy storage battery of the target photovoltaic storage system at time t according to the execution data : ;in, represents the actual state of charge of the energy storage battery of the target solar-storage system at time t, represents the state of charge of the energy storage battery of the target solar-storage system in the target day-ahead scheduling plan at time t; Calculate the load power deviation of the target solar-storage system at time t based on the execution data : ;in, represents the actual load power of the target solar-storage system at time t; represents the load prediction power of the target solar-storage system at time t; When it is detected that the triggering condition is sudden extreme weather, and / or emergency power outage, and / or the need to activate a backup power supply, determining that the triggering event is the first-level response event; or When it is detected that the power deviation is greater than a first threshold, and / or the state of charge deviation is greater than a second threshold, and / or the load power deviation is greater than a third threshold, the triggering event is determined to be the secondary response event.

7. A computer device, characterized in that: The computer device comprises: a memory storing at least one instruction; and The processor executes the instructions stored in the memory to implement the trigger mechanism-based photovoltaic storage system scheduling method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and the at least one instruction is executed by a processor in a computer device to implement the trigger mechanism-based photovoltaic storage system scheduling method according to any one of claims 1 to 5.

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